Risk Stratification to Decrease Unnecessary Diagnostic Imaging for Acute AppendicitisHolly Depinet, MD, MPH, Daniel von Allmen, MD, Alex Towbin, MD, Richard Hornung, PHD, Mona Ho, MS, Evaline Alessandrini, MD, MSCE

Cincinnati Children’s Hospital Medical Center, Cincinnati,

Ohio

Dr Depinet conceptualized and designed the

study, oversaw enrollment and data management,

analyzed data, and drafted the initial manuscript;

Drs von Allmen and Towbin participated in study

design, analyzed the data, and revised the initial

manuscript; Dr Hornung and Ms Ho participated

in study design, carried out the initial analyses,

and reviewed and revised the manuscript; Dr

Alessandrini conceptualized and designed the

study, analyzed the data, and revised the initial

manuscript; and all authors approved the fi nal

manuscript as submitted.

DOI: 10.1542/peds.2015-4031

Accepted for publication May 19, 2016

Address correspondence to Holly Depinet, MD,

MPH, Division of Emergency Medicine, CCHMC,

3333 Burnet Ave, Cincinnati, OH 45229. E-mail: holly.

depinet@cchmc.org

PEDIATRICS (ISSN Numbers: Print, 0031-4005; Online,

1098-4275).

Copyright © 2016 by the American Academy of

Pediatrics

FINANCIAL DISCLOSURE: The authors have

indicated they have no fi nancial relationships

relevant to this article to disclose.

FUNDING: No external funding.

POTENTIAL CONFLICT OF INTEREST: The authors

have indicated they have no potential confl icts of

interest to disclose.

Identifying the small number of

patients with appendicitis among the

many presenting to the emergency

department (ED) for evaluation

of pediatric abdominal pain 1 is

challenging. Although the accuracy

of clinical factors for diagnosing

appendicitis is reported at 70% to

87%, 2, 3 the use of diagnostic imaging

including computed tomography (CT)

has become widespread, increasing

141% from 1996 to 2005. 4 Despite

the current recommendation for

ultrasound as the preferred first

diagnostic imaging modality5 and

the small increase in lifetime risk of

malignancy associated with ionizing

radiation, 6 CT is still commonly used

in some areas for initial diagnostic

imaging. 7

abstractBACKGROUND: There has been an increase in the use of imaging modalities

to diagnose appendicitis despite evidence that can help identify children

at especially high or low risk of appendicitis who may not benefit. We

hypothesized that the passive diffusion of a standardized care pathway

(including diagnostic imaging recommendations) would improve the

diagnostic workup of appendicitis by safely decreasing the use of

unnecessary imaging when compared with historical controls and that an

electronic, real-time decision support tool would decrease unnecessary

imaging.

METHODS: We used an interrupted time series trial to compare proportions

of patients who underwent diagnostic imaging (computed tomography

[CT] and ultrasound) between 3 time periods: baseline historical controls,

after passive diffusion of a diagnostic workup clinical pathway, and after

introduction of an electronic medical record–embedded clinical decision

support tool that provides point-of-care imaging recommendations (active

intervention).

RESULTS: The moderate- and high-risk groups showed lower proportions of

CT in the passive and active intervention time periods compared with the

historical control group. Proportions of patients undergoing ultrasound

in all 3 risk groups showed an increase from the historical baseline. Time

series analysis confirmed that time trends within any individual time

period were not significant; thus, incidental secular trends over time did not

appear to explain the decreased use of CT.

CONCLUSIONS: Passive and active decision support tools minimized

unnecessary CT imaging; long-term effects remain an important area of

study.

QUALITY REPORTPEDIATRICS Volume 138 , number 3 , September 2016 :e 20154031

To cite: Depinet H, von Allmen D, Towbin A,

et al. Risk Stratifi cation to Decrease Unnecessary

Diagnostic Imaging for Acute Appendicitis.

Pediatrics. 2016;138(3):e20154031

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DEPINET et al

Clinical scoring systems have been

developed to identify patients

at especially high or low risk of

appendicitis based on clinical features,

including the 10-point Pediatric

Appendicitis Score (PAS). 8 Initially,

a single scoring cutoff was used, but

subsequent studies found better

validity when scores delineated 3 risk

strata. Specifically, high-risk scores

(≥7) have a specificity of 95% to

98%, 9, 10 and low-risk scores (≤3)

have a negative predictive value

of 98%, 10 identifying cases where

diagnostic imaging could be avoided.

Evidence-based clinical pathways

often standardize care and use

clinical decision support (CDS) tools

or systems to achieve their goals. 11 – 16

Studies have shown that CDS tools

that are presented to the clinician

through active interventions showed

improved outcomes, and early

evidence indicates that computerized

CDS systems that present

recommendations at the point of care

also increase adherence to specific

clinical recommendations. 17

We hypothesized that the passive

diffusion of a standardized care

pathway (including diagnostic

workup recommendations)

would safely decrease the use of

unnecessary imaging when compared

with baseline historical controls and

that an active intervention at the

point of care would decrease the use

of unnecessary imaging ( Fig 1).

METHODS

Setting

This study was conducted at an

urban tertiary care hospital with

a pediatric ED with 90 000 annual

visits and ~500 cases of appendicitis

per year. There is 24-hour availability

of surgical subspecialty consultation,

anesthesia, and radiology (including

ultrasound and CT). The ED used a

well-established electronic medical

record (EMR) system with decision

support capabilities throughout the

study period.

Study Design

This was a prospective, interrupted

time series trial comparing imaging

use during 3 time periods: a historical

e2

FIGURE 1Diagnostic pathway for patients age 3 to 21 with suspected acute (<72 hours) appendicitis.

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PEDIATRICS Volume 138 , number 3 , September 2016

baseline, after traditional passive

diffusion of a clinical pathway, and

after implementation of an active

CDS intervention that used the same

clinical pathway ( Fig 2).

Interventions

Historical Controls (January–December 2010)

We used a cohort with clinical

characteristics that approximated

those of our study sample, to

determine baseline rates of outcomes

before any interventions were

made. 18

Passive Intervention (January 2012–October 2012)

We developed a clinical pathway

for the diagnostic evaluation of

suspected acute appendicitis that

was based on the well-validated PAS

and incorporated our standardized

ultrasound report templates and

test characteristics. 19 The pathway

recommended minimizing imaging

in the high- and low-risk groups

and encouraged ultrasound over CT

use in the moderate risk category.

The passive intervention (typical

evidence dissemination) included

conventional passive diffusion of

the clinical pathway via review at

divisional meetings and educational

conferences, posted copies of the

pathway in clinical areas and on

our divisional Web site, and e-mail

reminders.

Active Intervention (October 2012–June 2013)

We implemented a real-time,

computerized CDS tool that

integrated the above appendicitis

clinical pathway at the point of

care in the EMR. The tool was

technically simple, and physicians

had used similar tools in our setting

previously. Workflow analysis and

focus groups with key stakeholders

informed the development of a CDS

tool with 3 interacting components,

which were extensively tested:

• EMR-based data collection template: An alert (which fired

in response to chief complaints:

vomiting and abdominal pain)

triggered clinicians to enter

elements of the PAS in a timely

fashion without interrupting their

clinical workflow; laboratory and

fever data were automatically

incorporated ( Fig 3). The tool

was iterative; if elements were

changed by the user or a more

senior physician, new data were

incorporated.

• CDS engine: The CDS engine

the above data and provided the

individualized risk score, strata,

and recommendations.

• EMR feedback interface: A

feedback interface provided the

results and recommendations

e3

FIGURE 2Number meeting inclusion and exclusion criteria in all 3 time intervals.

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DEPINET et al

to clinician users in a clear,

visually simple manner at a

logical point in the ED workflow;

recommendations were associated

with automatic orders (based on

age, gender, and PAS risk strata)

for the appropriate testing or

treatment, which the physician

could accept or modify ( Fig 4).

Study of the Intervention

Inclusion and Exclusion Criteria and Population Identifi cation

Our study included 3 time periods:

historical baseline, passive

intervention, and active intervention.

During the active and passive

intervention phases, we included

a convenience sample of patients

age 3 to 21, with symptoms for

<72 hours, where the ED attending

physician had a clinical suspicion for

appendicitis or the patient had right

lower quadrant pain. We excluded

patients with significant abdominal

trauma, outside institution imaging,

or an underlying medical condition

that can confound the diagnosis of

appendicitis.

A paper data collection tool was

used to collect prospective data

for analysis (PASs determined by a

combination of attending physician

impression of clinical variables

at the time of their first physical

examination and laboratory and vital

sign data) during both intervention

phases for 2 reasons. First, this step

ensured that the data in both time

periods were measured similarly;

additionally, it allowed us to study

the active intervention (the CDS

tool) itself by comparing scores

obtained from the CDS tool with

the prospective data. To minimize

the influence of this data collection

tool (ie, limit the possibility that it

would serve as an intervention itself),

we included extraneous variables

and had a run-in period before the

passive intervention started. Because

our clinical pathway did not firmly

recommend a complete blood cell

count (CBC) for low-risk patients, in

cases where a CBC was not ordered,

we analyzed the PAS without

complete data.

To find historical controls that

represented the true baseline before

the clinical pathway was developed

(the pathway was developed during

the year before the interventions),

thus before any clinicians had

knowledge of its recommendations,

we sought a population cared for

>1 year before our study began.

Identifying such a population is

challenging, because patients with

issues of diagnostic accuracy 20

such as “potential appendicitis” are

difficult to identify retrospectively.

e4

FIGURE 3Chief complaint–based alert notifying clinicians to use the CDS tool (top) and data collection screen in the CDS tool (bottom).

FIGURE 4Automatic orders generated by the CDS tool (based on patient age, gender, and PAS risk strata).

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PEDIATRICS Volume 138 , number 3 , September 2016

To identify a historical population

that would accurately approximate

our study sample, we used a database

of ED patients with abdominal pain,

from a 1-year period that preceded

our study by 13 months, who had

PAS assigned and validated by chart

review by pediatric emergency

medicine providers as part of another

study. 18 To select the patients in

this database who were as similar

as possible to our study population,

we additionally analyzed charts

to determine whether patients

would have met study inclusion and

exclusion criteria.

Specifically, ED physician notes

were analyzed via a natural

language processing program to

identify any mention of concern for

appendicitis; this is a well-validated

technique to extract relevant clinical

information from free text notes, and

in a previous study that used this

database, it performed comparably

to physician chart reviewers in

identifying clinical elements of the

PAS. 18 Additionally, we reviewed

imaging orders and indications. This

database fully sampled patients

with a diagnosis of appendicitis

and also included a random sample

of other patients with abdominal

pain. Therefore, the moderate- and

high-risk patients in the historical

control group approximate the same

groups in our study sample well. The

low-risk group was proportionally

larger in the historical sample, but it

includes patients with a low risk of

appendicitis based on our criteria.

Measurement

Outcome Variables

Our primary outcome of interest

was the proportion of patients

who underwent CT as part of their

evaluation for suspected appendicitis.

Outcomes were calculated overall

and stratified by high, moderate, or

low risk for appendicitis. Secondary

outcomes were proportion

undergoing ultrasound and ED length

of stay (LOS).

Additional process measures

included use of the CDS tool,

agreement between the CDS tool

and prospectively collected data

elements, and presence of a CBC.

Balancing measures include negative

appendectomy rate and missed

diagnosis rate (based on 1-month

follow-up review).

Covariates included age, gender,

race, shift of arrival (day, evening,

or overnight), resident physician

involvement (yes or no), and

referral status (referred or not

referred).

Analysis

Outcomes were examined via

statistical process control analyses,

logistic regression, and a time series

analysis to assess for trends within

each time period. Specifically, we

used p-charts, which are a type of

Shewhart chart (commonly called

a control chart) used to graphically

display trends and explore variation

in binomial data (eg, CT no CT). 21

We ran separate multivariable

logistic regression models for the

outcomes of CT and ultrasound

by using separate 3-level dummy

variables for the variable

representing PAS risk group (high,

moderate, or low) and intervention

time interval (historical, passive

diffusion, or active intervention)

across time intervals and stratified

by risk group, controlled for all

covariates.

Time series analyses assessed for

secular trends in major outcomes

between study time periods

(via comparison of means) and

within each study time period (via

comparison of slopes). This was

done to expand on the results of the

regression analyses and determine

whether our outcomes were stable

within each time period or changing

incrementally over time and thus

help determine whether the changes

we saw between time periods

were probably attributable to our

intervention or to secular trends

already under way.

We determined frequency of CDS

use and compared PAS risk strata

generated by the CDS tool with risk

strata determined by the prospective

data collection. ED LOS was reported

for each time period, stratified by

PAS risk group.

RESULTS

Our final sample included 809

patients in the historical group,

588 in the passive intervention

group, and 489 in the active

intervention group ( Fig 2). Groups

were similar with respect to age,

gender, and race; however, patients

in the active and passive groups

were more likely to have had a

resident physician involved in their

care, to have been referred to the

ED, and to have presented to the

ED earlier in the day compared with

historical controls. Additionally,

the historical group had a higher

proportion of patients in the low-risk

strata ( Table 1).

The CDS tool was used 61% of time in

the active study time period; the final

PAS strata assigned by the CDS tool

(including patients where all clinical

PAS elements were completed by the

physician user) were in agreement

with the prospectively assigned PAS

84.5% of the time; however, in 16%

of cases the CDS tool had ≥1 missing

or unknown clinical variable ( Table 1).

P-charts showed a decrease in the

proportion of patients with CT from

the historical baseline to the passive

and active intervention phases, in the

moderate-risk (from 22.3% to 10.2%

and 12.2%) and high-risk (from 25.2%

to 15.7% and 14.4%) strata ( Figs 5

and 6). There were too few patients

in the low-risk strata to create control

charts. Ultrasound increased in all

groups combined (we combined this

graphical display for all 3 groups

because similar statistically significant

increases were seen in all groups over

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DEPINET et al

time) from 59.5% in the historical

group to 83.7% in the passive group

and 88.8% in the active group ( Fig 7).

In the regression analysis, moderate-

and high-risk patients during both

the active and passive phases had

significantly lower proportions of

CTs than those in the historical time

period (but neither differed from

passive to active phases); all risk

groups during both the active and

passive phases had significantly

higher proportions of ultrasounds

than those in the historical time

period ( Table 2).

Time series analyses found that

a comparison of the slopes of the

proportion of patients with imaging

within each time period was

nonsignificant, so changes seen were

not thought to be attributable to

secular trends occurring incidentally.

In the time series comparison of the

e6

TABLE 1 Characteristics of the Study Population (With 95% Confi dence Intervals)

Historical, n = 809 Passive, n = 588 Active, n = 489

Age, y (median) 12 12 12

Gender, % male 48.2% (44.8–51.7) 43.5% (39.5–47.5) 46.6% (42.2–51.1)

Race, % white 78.1% (75.3–81.0) 76.6% (73.1–79.9) 77.9% (74.2–81.6)

Resident involved, % yes 29.5% (26.4–32.7) 70.6% (66.9–74.3) 77.5% (73.8–81.2)

Referral, % yes 15.6% (13.1–18.1) 56.8% (52.8–60.8) 58.7% (54.3–63.1)

PAS distribution

Low risk 18.9% (16.2–21.6) 10.4% (7.9–12.8) 6.8% (4.5–9.0)

Moderate risk 38.8% (35.5–42.2) 51.9% (47.8–55.9) 60.5% (56.2–64.9)

High risk 42.3% (38.9–45.7) 37.8% (33.8–41.7) 32.7% (28.6–36.9)

Time of day of arrival

Day 42.4% (39.0–45.8) 60.4% (56.4–64.3) 58.5% (54.1–62.9)

Evening 41.9% (38.5–45.3) 34.9% (31.0–38.7) 34.4% (30.1–38.6)

Overnight 15.7% (13.2–18.2) 4.8% (3.0–6.5) 7.2% (4.9–9.4)

% of time CDS tool used n/a n/a 61.6% (57.2–65.9)

% with CBC 62.8% (59.5–66.1) 71.9%(68.3–75.6) 76.9% (73.2–80.1)

Negative appendectomy ratea 17.2% (10.7–23.7) 8.6% (4.6–12.6) 16.8% (12.9–20.7)

Missed appendicitis rateb n/a 0 0

n/a, not applicable.a Based on operative histology.b Based on 1-mo follow-up.

FIGURE 5P-chart for proportion of patients with CT imaging over time in high-risk patients (subgroups of 20).

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PEDIATRICS Volume 138 , number 3 , September 2016 e7

FIGURE 6P-chart for proportion of patients with CT imaging over time in moderate-risk patients (subgroups of 20).

FIGURE 7P-chart for the proportion of patients with ultrasound over time in all risk groups (subgroups of 20).

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DEPINET et al

means between study time periods,

only the proportion of CT in the

moderate risk group decreased

(P = .03).

Analysis of ED LOS showed no

appreciable change over the 3 time

periods ( Table 3).

DISCUSSION

We found lower rates of CT imaging

the moderate- and high-risk strata,

in the passive diffusion and active

intervention groups compared

with historical controls, and an

unintended consequence of an

increase in the use of ultrasound.

The additional ultrasounds could

be explained by several factors:

Some patients in the final high-risk

category did not have laboratory

results ready at the time of ordering

an ultrasound, and so they appeared

to be in the moderate-risk category

until their CBC was completed

(the CDS tool was iterative and

would have given preliminary

recommendations for imaging even

without final data); also, trainees

may have scored the patients

higher than attending physicians.

Finally, some high-risk patients

may have appropriately had

ultrasound ordered after

surgical consultation.

The CDS tool was used 61% of

the time in the active time period,

and the CDS-assigned risk strata

correlated with prospective data

collected 84.4% of the time. Although

the CDS tool offered patient-tailored

recommendations at the point

of care, challenges of real-world

implementation may have led to

less than ideal practice (eg, missing

or unknown variables entered

into the CDS tool). For instance,

clinicians may have believed that

the PAS strata were simple enough

to memorize, and therefore they

did not need decision support, or

the many rotating trainees from

various institutions may have been

less familiar with CDS tools or not

reliably trained to use them. Also,

the firing of the CDS tool each time

there was a chief complaint related

to appendicitis may have led to alert

fatigue. Overall, more work is needed

to introduce a culture of standardized

care in which such a decision support

tool could work optimally.

Although these results showed

improved care in the direction

we thought was most important

(minimizing diagnostic imaging

associated with ionizing radiation),

we did not see more improvement

in imaging rates from the passive to

the active intervention time periods,

as we hypothesized we would. Active

interventions, especially those

embedded in the EMR at the point

of decision making, have shown

improved outcomes over guidelines

alone. 15, 22 – 24 It is possible that the

initial positive effect of traditional

passive diffusion of knowledge

extinguishes over time25; because our

passive pathway was present for only

9 months before the introduction of

the active pathway, it was still fairly

novel to clinicians, and its effects

did not have time to extinguish.

Additionally, it is possible that all

the preliminary work that occurred

in the year between the historical

control period and the passive and

active intervention periods, which

enabled development of the clinical

pathway for implementation (eg,

obtaining consensus among specialty

services about imaging goals,

changes in workflow and processes

for multiple divisions to support

the pathway, education about the

utility of the PAS and improved

test characteristics with standardized

ultrasound interpretations),

changed our practice in a way that

is more sustainable than an

individual pathway. We did not

see a significant difference in

e8

TABLE 2 Multivariablea Regression for the Outcomes of CT and Ultrasound

Historical Controls, n = 809 Passive Diffusion, n = 588 Active Intervention, n = 489

OR OR 95% CI OR 95% CI

CT

Low riskb 1.0 3.27 0.85–12.6 3.71 0.77–17.5

Moderate risk 1.0 0.38 0.24–0.61 0.48 0.31–0.75

High risk 1.0 0.55 0.36–0.85 0.49 0.3–0.82

Ultrasound

Low risk 1.0 10.5 5.0–22.0 10.8 4.2–27.5

Moderate risk 1.0 8.4 4.8–14.7 17.8 9.5–33.3

High risk 1.0 14.9 7.4–29.8 18.3 8.6–39

CI, confi dence interval; OR, odds ratio.a Adjusted for age, gender, race, shift of arrival (day, evening, or overnight), resident physician involvement (yes or no), and referral status (referred or not referred).b All risk strata based on our institutional pathway.

TABLE 3 ED LOS for Patients in All Risk Strata Across Time Intervals

Historical Controls,

n = 809

Passive Diffusion,

n = 588

Active Intervention,

n = 489

Mean (95% CI) Mean (95% CI) Mean (95% CI)

LOS, min, high risk 355.5 (339.4–371.6) 367.8 (350.4–385.3) 372.9 (351.4–394.4)

LOS, min, moderate risk 354.3 (337.9–370.6) 336.5 (321.8–351.2) 371.3 (355.7–386.9)

LOS, min, low risk 251.7 (234.6–268.7) 325.4 (284.5–366.3) 289.6 (244.0–335.1)

CI, confi dence interval.

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PEDIATRICS Volume 138 , number 3 , September 2016

ED LOS between the time intervals;

however, this factor is affected by

many variables other than the

targets of our interventions

(eg, hospital boarding, changes in

available rooms due to construction,

other initiatives).

One challenge in studies of diagnostic

accuracy 20 is the difficulty with

population identification. Unlike

in studies of children with known

diagnoses such as asthma or diabetes,

there is no simple way to identify

patients with “possible diagnoses, ”

such as appendicitis, intussusception,

or sepsis. Our method of population

identification for the active and

passive samples (asking the

attending physician whether he or

she is considering appendicitis) has

been used successfully in previous

studies of pediatric appendicitis and

other types of studies. 26, 27 However,

there is a risk that this method may

introduce bias (toward the null) by

prompting the ED attending use the

pathway.

Additionally, we believe that the

low-risk population, in which

the clinician has some thought of

appendicitis but has ruled it out

clinically, may be underrepresented

in the passive and active diffusion

phases because of the hesitation to

identify a patient as having some

risk of appendicitis (however low)

despite not planning to do any more

evaluation. Thus, the historical

sample, with its population identified

by less subjective methods, may

represent a truer low-risk sample

than the prospectively identified

samples.

Challenges with population

identification also led to the need

to approximate the historical

control group differently than the

intervention groups, which may

introduce bias. For instance, the

historical group included more

patients seen overnight, fewer

patients cared for by resident

physicians, and patients from all

areas of the ED (whereas the active

and passive study populations

were not drawn from urgent

care areas). These factors could

have affected imaging outcomes

in the historical group, although

this effect is minimized by close

supervision of learners, introduction

of the pathway to residents, and

constant availability of ultrasound.

Additionally, some of these factors

would theoretically have introduced

bias in differing directions (ie, some

would bias toward and some away

from the null), but we believe that

our stringent selection criteria for

historical controls minimized this

possibility and that the moderate-

and high-risk groups were similar

across study periods.

Additionally, although a decrease in

CT was temporally correlated with

our interventions, it is possible that

other more global factors, such as

nationally disseminated knowledge

about the risks of CT, were partially

responsible for the decrease in CT

that we observed.

Finally, generalizability may be

limited to institutions with the

specific capabilities needed for

the intervention: 24-hour availability

of surgeons and radiologists,

acceptable test characteristics

of ultrasound, and EMRs with

decision support capabilities

and the technical resources to

program them.

CONCLUSIONS

Overall, we saw an improvement

in the use of CT from our historical

baseline, although CT use did not

differ between active and passive

intervention groups, and we saw an

unintended consequence of increased

use of ultrasound. Additional work

is needed to determine whether

this effect will diminish over time.

Additionally, this study demonstrates

the value of standardized care

pathways in reducing variation

in imaging in cases of diagnostic

accuracy.

REFERENCES

1. Pitts SR, Niska RW, Xu J, Burt CW.

National Hospital Ambulatory Medical

Care Survey: 2006 emergency

department summary. Natl Health Stat

Report. 2008;(7)1–38

2. Andersson RE. Meta-analysis

of the clinical and laboratory

diagnosis of appendicitis. Br J Surg.

2004;91(1):28–37

3. Toorenvliet BR, Wiersma F, Bakker

RF, Merkus JW, Breslau PJ, Hamming

JF. Routine ultrasound and limited

computed tomography for the

diagnosis of acute appendicitis. World

J Surg. 2010;34(10):2278–2285

4. Pines JM. Trends in the rates of

radiography use and important

diagnoses in emergency department

patients with abdominal pain. Med

Care. 2009;47(7):782–786

5. Howell JM, Eddy OL, Lukens TW,

Thiessen ME, Weingart SD, Decker

WW; American College of Emergency

Physicians. Clinical policy:

critical issues in the evaluation

and management of emergency

department patients with suspected

appendicitis. Ann Emerg Med.

2010;55(1):71–116

6. Brenner D, Elliston C, Hall E, Berdon W.

Estimated risks of radiation-induced

fatal cancer from pediatric CT.

AJR Am J Roentgenol. 2001;176(2):

289–296

7. Fullerton KT, Depinet H, Iyer S, Boeck

RN, Herr S, Morton I. Imaging variation

in the diagnosis of acute appendicitis.

AAP National Conference & Exhibition;

October 2015; Washington, DC

8. Samuel M. Pediatric appendicitis

score. J Pediatr Surg.

2002;37(6):877–881

9. Goldman RD, Carter S, Stephens D,

Antoon R, Mounstephen W, Langer JC.

e9

ABBREVIATIONS

CBC:  complete blood cell count

CDS:  clinical decision support

CT:  computed tomography

ED:  emergency department

EMR:  electronic medical record

LOS:  length of stay

PAS:  Pediatric Appendicitis Score

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DEPINET et al

Prospective validation of the pediatric

appendicitis score. J Pediatr.

2008;153(2):278–282

10. Bhatt M, Joseph L, Ducharme FM,

Dougherty G, McGillivray D. Prospective

validation of the pediatric appendicitis

score in a Canadian pediatric

emergency department. Acad Emerg

Med. 2009;16(7):591–596

11. Bergman DA. Evidence-based

guidelines and critical pathways for

quality improvement. Pediatrics.

1999;103(1 suppl E):225–232

12. Institute of Medicine. To Err Is Human:

Building a Safer Health System.

Washington, DC: National Academy

Press; 1999

13. Chaudhry B, Wang J, Wu S, et al.

Systematic review: impact of health

information technology on quality,

effi ciency, and costs of medical care.

Ann Intern Med. 2006;144(10):742–752

14. Osheroff JA, Teich JM, Levick D, et

al. Improving Outcomes With Clinical

Decision Support: An Implementer’s

Guide. Chicago, IL: Healthcare

Information and Management Systems

Society; 2005

15. Osheroff JA, Teich JM, Middleton

B, Steen EB, Wright A, Detmer DE. A

roadmap for national action on clinical

decision support. J Am Med Inform

Assoc. 2007;14(2):141–145

16. Wright SW, Trott A, Lindsell CJ, Smith C,

Gibler WB. Evidence-based emergency

medicine. Creating a system to

facilitate translation of evidence

into standardized clinical practice: a

preliminary report. Ann Emerg Med.

2008;51(1):80–86, 86.e1–86.e8

17. Shojania KG, Jennings A, Mayhew A,

Ramsay CR, Eccles MP, Grimshaw J.

The effects of on-screen, point of care

computer reminders on processes and

outcomes of care. Cochrane Database

Syst Rev. 2009; (3):CD001096

18. Deleger L, Brodzinski H, Zhai H,

et al. Developing and evaluating

an automated appendicitis risk

stratifi cation algorithm for

pediatric patients in the emergency

department. J Am Med Inform Assoc.

2013;20(e2):e212–e220

19. Larson DB, Trout AT, Fierke SR,

Towbin AJ. Improvement in diagnostic

accuracy of ultrasound of the

pediatric appendix through the use

of equivocal interpretive

categories. AJR Am J Roentgenol.

2015;204(4):849–856

20. Iyer S, Reeves S, Varadarajan K,

Alessandrini E. The acute care model:

a new framework for quality care

in emergency medicine. Clin Pediatr

Emerg Med. 2011;12(2):91–101

21. Provost LP. Murray SK. Heath Care Data

Guide. San Francisco, CA: Jossey-Bass;

2011

22. Bates DW, Kuperman GJ, Wang S,

et al. Ten commandments for effective

clinical decision support: making the

practice of evidence-based medicine

a reality. J Am Med Inform Assoc.

2003;10(6):523–530

23. Sintchenko V, Coiera E, Iredell JR,

Gilbert GL. Comparative impact of

guidelines, clinical data, and decision

support on prescribing decisions:

an interactive Web experiment with

simulated cases. J Am Med Inform

Assoc. 2004;11(1):71–77

24. Kawamoto K, Houlihan CA, Balas EA,

Lobach DF. Improving clinical practice

using clinical decision support

systems: a systematic review of trials

to identify features critical to success.

BMJ. 2005;330(7494):765

25. Yang CW, Yen ZS, McGowan JE,

et al. A systematic review of

retention of adult advanced life

support knowledge and skills in

healthcare providers. Resuscitation.

2012;83(9):1055–1060

26. Kuppermann N, Holmes JF, Dayan

PS, et al; Pediatric Emergency Care

Applied Research Network (PECARN).

Identifi cation of children at very

low risk of clinically-important

brain injuries after head trauma: a

prospective cohort study. Lancet.

2009;374(9696):1160–1170

27. Yen K, Kuppermann N, Lillis K.

Interobserver agreement in the clinical

assessment of children with blunt

abdominal truma. Acad Emerg Med.

2013;20(5):426–432

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DOI: 10.1542/peds.2015-4031 originally published online August 23, 2016; 2016;138;Pediatrics 

Evaline AlessandriniHolly Depinet, Daniel von Allmen, Alex Towbin, Richard Hornung, Mona Ho and

AppendicitisRisk Stratification to Decrease Unnecessary Diagnostic Imaging for Acute

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Evaline AlessandriniHolly Depinet, Daniel von Allmen, Alex Towbin, Richard Hornung, Mona Ho and

AppendicitisRisk Stratification to Decrease Unnecessary Diagnostic Imaging for Acute

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